The University of Southampton
University of Southampton Institutional Repository

Extended state observer based indirect-type ILC for single-input single-output batch processes with time- and batch-varying uncertainties

Extended state observer based indirect-type ILC for single-input single-output batch processes with time- and batch-varying uncertainties
Extended state observer based indirect-type ILC for single-input single-output batch processes with time- and batch-varying uncertainties
In this paper, a set-point related indirect-type iterative learning control (ILC) design is proposed for industrial batch processes with time-varying uncertainties and external disturbances. Different from the existing robust feed forward ILC methods solely focusing on error convergence along the batch
direction, the proposed design has a double-loop control structure to conduct also dynamic control performance in the time direction as required in many engineering applications, where the inner loop is a generalized extended state observer based feedback control structure designed to ensure set-point
tracking with robust stability in the time direction, and the outer loop consists of a simple proportionaltype learning controller to update only the set-point command such that the tracking performance can be gradually improved along the batch direction. A tractable linear matrix inequality based sufficient
condition is established to simultaneously guarantee bounded output tracking error and system input against time- and batch-varying uncertainties. An industrial injection molding process model is used to demonstrate the effectiveness and advantages of the new design in comparison to the recently
developed robust feedforward ILC and indirect-type ILC designs.
0005-1098
Hao, Shoulin
68222d62-1397-4e66-ad0f-56caa67bd9e2
Liu, Tao
0c29c130-c388-491c-81ef-a15a9349ac4b
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Hao, Shoulin
68222d62-1397-4e66-ad0f-56caa67bd9e2
Liu, Tao
0c29c130-c388-491c-81ef-a15a9349ac4b
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72

Hao, Shoulin, Liu, Tao and Rogers, Eric (2020) Extended state observer based indirect-type ILC for single-input single-output batch processes with time- and batch-varying uncertainties. Automatica, 112, [108673]. (doi:10.1016/j.automatica.2019.108673).

Record type: Article

Abstract

In this paper, a set-point related indirect-type iterative learning control (ILC) design is proposed for industrial batch processes with time-varying uncertainties and external disturbances. Different from the existing robust feed forward ILC methods solely focusing on error convergence along the batch
direction, the proposed design has a double-loop control structure to conduct also dynamic control performance in the time direction as required in many engineering applications, where the inner loop is a generalized extended state observer based feedback control structure designed to ensure set-point
tracking with robust stability in the time direction, and the outer loop consists of a simple proportionaltype learning controller to update only the set-point command such that the tracking performance can be gradually improved along the batch direction. A tractable linear matrix inequality based sufficient
condition is established to simultaneously guarantee bounded output tracking error and system input against time- and batch-varying uncertainties. An industrial injection molding process model is used to demonstrate the effectiveness and advantages of the new design in comparison to the recently
developed robust feedforward ILC and indirect-type ILC designs.

Text
auto20 - Version of Record
Restricted to Repository staff only
Request a copy

More information

Published date: 20 June 2020

Identifiers

Local EPrints ID: 453989
URI: http://eprints.soton.ac.uk/id/eprint/453989
ISSN: 0005-1098
PURE UUID: df04ddaa-9884-4d26-ae23-074f654776fd
ORCID for Eric Rogers: ORCID iD orcid.org/0000-0003-0179-9398

Catalogue record

Date deposited: 27 Jan 2022 18:09
Last modified: 28 Apr 2022 01:35

Export record

Altmetrics

Contributors

Author: Shoulin Hao
Author: Tao Liu
Author: Eric Rogers ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×